llgyuleall {degreenet} | R Documentation |
Functions to Estimate the Log-likelihood for Discrete Probability Distributions Based on Categorical Response.
llgyuleall(v, x, cutoff = 2, cutabove = 1000, np=1)
v |
A vector of parameters for the Yule (a 1-vector - the scaling exponent). |
x |
A vector of categories for counts (one per observation). The values of |
cutoff |
Calculate estimates conditional on exceeding this value. |
cutabove |
Calculate estimates conditional on not exceeding this value. |
np |
wnumber of parameters in the model. For the Yule this is 1. |
the log-likelihood for the data x
at parameter value v
.
See the working papers on http://www.csss.washington.edu/Papers for details
Jones, J. H. and Handcock, M. S. "An assessment of preferential attachment as a mechanism for human sexual network formation," Proceedings of the Royal Society, B, 2003, 270, 1123-1128.
gyulemle, llgyule, dyule, llgwarall
# # Simulate a Yule distribution over 100 # observations with rho=4.0 # set.seed(1) s4 <- simyule(n=100, rho=4) table(s4) # # Recode it as categorical # s4[s4 > 4 & s4 < 11] <- 5 s4[s4 > 100] <- 8 s4[s4 > 20] <- 7 s4[s4 > 10] <- 6 # # Calculate the MLE and an asymptotic confidence # interval for rho # s4est <- gyulemle(s4) s4est # Calculate the MLE and an asymptotic confidence # interval for rho under the Waring model (i.e., rho=4, p=2/3) # s4warest <- gwarmle(s4) s4warest # # Compare the AICC and BIC for the two models # llgyuleall(v=s4est$theta,x=s4) llgwarall(v=s4warest$theta,x=s4)